{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Dataset & Create Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "\n",
    "import pandas as pd\n",
    "import numpy  as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = \"datasets/housing\"\n",
    "HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + \"/housing.tgz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fetch_housing_data(\n",
    "    housing_url=HOUSING_URL, \n",
    "    housing_path=HOUSING_PATH):\n",
    "    \n",
    "    # create datasets/housing directory if needed\n",
    "    if not os.path.isdir(housing_path):\n",
    "        os.makedirs(housing_path)\n",
    "\n",
    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
    "    \n",
    "    # retrieve tarfile\n",
    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
    "    \n",
    "    # extract tarfile & close path\n",
    "    housing_tgz = tarfile.open(tgz_path)\n",
    "    housing_tgz.extractall(path=housing_path)\n",
    "    housing_tgz.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_housing_data(\n",
    "    housing_path=HOUSING_PATH):\n",
    "    \n",
    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# do it\n",
    "#fetch_housing_data() -- already downloaded - static dataset\n",
    "housing = load_housing_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data structure - quick peek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So... what's in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# housing is a Pandas DataFrame.\n",
    "# untouched datafile: 20640 records, 10 cols (9 float, 1 text)\n",
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see if ocean_proximity can be lumped into categories:\n",
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentiles analysis of each feature\n",
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a792b5438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75f1c2e8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75f39d68>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75eaf7b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75e7acc0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75e40438>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75e0a860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75dce198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4a75d1d1d0>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tuqFn+a4t5w05EkmSJB0Jk0iSJEnLwFxJPEmSpPmyO5skSZIkSZL6MokkSZIk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\na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SNgsX8HTf6sHzb3DxpFqarF21jy08DaqvqF9vzVwA9U1b+ZtNxGYGN7+gLg3nls7jnA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a79541588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature histograms\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "housing.hist(bins=50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split dataset into training (80%) and test (20%) subsets\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def split_train_test(\n",
    "    data, test_ratio):\n",
    "\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test(housing, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16512 train + 4128 test\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create method for ensuring consistent test sets across multiple runs \n",
    "# (new test sets won't contain instances in previous training sets.)\n",
    "\n",
    "# example method:\n",
    "# compute hash of each instance\n",
    "# keep only the last byte\n",
    "# include instance in test set if value < 51 (20% of 256)\n",
    "\n",
    "import hashlib\n",
    "\n",
    "def test_set_check(\n",
    "    identifier, test_ratio, hash):\n",
    "    \n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio\n",
    "\n",
    "def split_train_test_by_id(\n",
    "    data, test_ratio, id_column, hash=hashlib.md5):\n",
    "\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(\n",
    "        lambda id_: test_set_check(\n",
    "            id_, test_ratio, hash))\n",
    "\n",
    "    return data.loc[~in_test_set], data.loc[in_test_set]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# housing dataset doesn't have ID attribute,\n",
    "# so let's add an index to it.\n",
    "\n",
    "housing_with_id = housing.reset_index()\n",
    "\n",
    "train_set, test_set = split_train_test_by_id(\n",
    "    housing_with_id, 0.2, \"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>40.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.3578</td>\n",
       "      <td>147500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2.1806</td>\n",
       "      <td>99700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "4       4    -122.25     37.85                52.0       1627.0   \n",
       "5       5    -122.25     37.85                52.0        919.0   \n",
       "11     11    -122.26     37.85                52.0       3503.0   \n",
       "20     20    -122.27     37.85                40.0        751.0   \n",
       "23     23    -122.27     37.84                52.0       1688.0   \n",
       "\n",
       "    total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "4            280.0       565.0       259.0         3.8462            342200.0   \n",
       "5            213.0       413.0       193.0         4.0368            269700.0   \n",
       "11           752.0      1504.0       734.0         3.2705            241800.0   \n",
       "20           184.0       409.0       166.0         1.3578            147500.0   \n",
       "23           337.0       853.0       325.0         2.1806             99700.0   \n",
       "\n",
       "   ocean_proximity  \n",
       "4         NEAR BAY  \n",
       "5         NEAR BAY  \n",
       "11        NEAR BAY  \n",
       "20        NEAR BAY  \n",
       "23        NEAR BAY  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# a better index:\n",
    "# let's use longitude & latitude to build stable identifier\n",
    "\n",
    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
    "\n",
    "train_set, test_set = split_train_test_by_id(\n",
    "    housing_with_id, 0.2, \"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.2031</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2.6736</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122222.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "8       8    -122.26     37.84                42.0       2555.0   \n",
       "10     10    -122.26     37.85                52.0       2202.0   \n",
       "11     11    -122.26     37.85                52.0       3503.0   \n",
       "12     12    -122.26     37.85                52.0       2491.0   \n",
       "13     13    -122.26     37.84                52.0        696.0   \n",
       "\n",
       "    total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "8            665.0      1206.0       595.0         2.0804            226700.0   \n",
       "10           434.0       910.0       402.0         3.2031            281500.0   \n",
       "11           752.0      1504.0       734.0         3.2705            241800.0   \n",
       "12           474.0      1098.0       468.0         3.0750            213500.0   \n",
       "13           191.0       345.0       174.0         2.6736            191300.0   \n",
       "\n",
       "   ocean_proximity         id  \n",
       "8         NEAR BAY -122222.16  \n",
       "10        NEAR BAY -122222.15  \n",
       "11        NEAR BAY -122222.15  \n",
       "12        NEAR BAY -122222.15  \n",
       "13        NEAR BAY -122222.16  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# another option: scikit-learn splitters\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(\n",
    "    housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>-119.01</td>\n",
       "      <td>36.06</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1505.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1.6812</td>\n",
       "      <td>47700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>-119.46</td>\n",
       "      <td>35.14</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>2.5313</td>\n",
       "      <td>45800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15663</th>\n",
       "      <td>-122.44</td>\n",
       "      <td>37.80</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3830.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>963.0</td>\n",
       "      <td>3.4801</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20484</th>\n",
       "      <td>-118.72</td>\n",
       "      <td>34.28</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3051.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>5.7376</td>\n",
       "      <td>218600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9814</th>\n",
       "      <td>-121.93</td>\n",
       "      <td>36.62</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2351.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>3.7250</td>\n",
       "      <td>278000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "20046    -119.01     36.06                25.0       1505.0             NaN   \n",
       "3024     -119.46     35.14                30.0       2943.0             NaN   \n",
       "15663    -122.44     37.80                52.0       3830.0             NaN   \n",
       "20484    -118.72     34.28                17.0       3051.0             NaN   \n",
       "9814     -121.93     36.62                34.0       2351.0             NaN   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "20046      1392.0       359.0         1.6812             47700.0   \n",
       "3024       1565.0       584.0         2.5313             45800.0   \n",
       "15663      1310.0       963.0         3.4801            500001.0   \n",
       "20484      1705.0       495.0         5.7376            218600.0   \n",
       "9814       1063.0       428.0         3.7250            278000.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "20046          INLAND  \n",
       "3024           INLAND  \n",
       "15663        NEAR BAY  \n",
       "20484       <1H OCEAN  \n",
       "9814       NEAR OCEAN  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14196</th>\n",
       "      <td>-117.03</td>\n",
       "      <td>32.71</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>3.2596</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8267</th>\n",
       "      <td>-118.16</td>\n",
       "      <td>33.77</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3382.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>382100.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17445</th>\n",
       "      <td>-120.48</td>\n",
       "      <td>34.66</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>915.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>4.1563</td>\n",
       "      <td>172600.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14265</th>\n",
       "      <td>-117.11</td>\n",
       "      <td>32.69</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1421.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>1.9425</td>\n",
       "      <td>93400.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>-119.80</td>\n",
       "      <td>36.78</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>3.5542</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "14196      2300.0       623.0         3.2596            103000.0   \n",
       "8267       1314.0       756.0         3.8125            382100.0   \n",
       "17445       915.0       336.0         4.1563            172600.0   \n",
       "14265      1418.0       355.0         1.9425             93400.0   \n",
       "2271        874.0       380.0         3.5542             96500.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "14196      NEAR OCEAN  \n",
       "8267       NEAR OCEAN  \n",
       "17445      NEAR OCEAN  \n",
       "14265      NEAR OCEAN  \n",
       "2271           INLAND  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f15f7250588>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sOHXW351/5O7rz+rx9c7hMwdJUsNykCQ1LAdJUsNykCQ1LAdJUsNykCQ1LAdJUsNykCQ1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\nhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15f7250e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# does sampling plan have a sampling bias?\n",
    "# each strata in test dataset should mimic reality\n",
    "\n",
    "housing['median_income'].hist(bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "housing[\"income_cat\"]=np.ceil(housing[\"median_income\"]/1.5)\n",
    "\n",
    "housing[\"income_cat\"].where(housing[\"income_cat\"]<5, 5.0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "      <td>3.006686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "      <td>1.054618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value    income_cat  \n",
       "count        20640.000000  20640.000000  \n",
       "mean        206855.816909      3.006686  \n",
       "std         115395.615874      1.054618  \n",
       "min          14999.000000      1.000000  \n",
       "25%         119600.000000      2.000000  \n",
       "50%         179700.000000      3.000000  \n",
       "75%         264725.000000      4.000000  \n",
       "max         500001.000000      5.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(\n",
    "    n_splits=1, test_size=0.2, random_state=42)\n",
    "\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set  = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.350581\n",
       "2.0    0.318847\n",
       "4.0    0.176308\n",
       "5.0    0.114438\n",
       "1.0    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review income category proportions\n",
    "housing[\"income_cat\"].value_counts() / len(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove income_cat attribute (return dataset to original state)\n",
    "\n",
    "for set in (strat_train_set, strat_test_set):\n",
    "    set.drop([\"income_cat\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f15f71c4668>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1T5bPN4WlSuNOBOsg94kHg5goVSRKM0xSPjoc8VF7xDDOWKp4D8yjPk8zarcb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fMLW3Ewm5XEmrq254nmjbiTVUlvAE/d4p6jd+r9FYr9yq2SnP3HB3M2L5wa4VPPDPN/vbx4F0e4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\n9Ao4MtL/XQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1625dc3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing = strat_train_set.copy()\n",
    "\n",
    "# first: basic geographic distribution\n",
    "\n",
    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f15f5eff1d0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8opIFnbkwfxZUVqjtRo9WDm4hVKvg9eth20H4+CNDOTSEVDHLfgNcIWpMB0/9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fnd5ac/HebIkTxSQne3wYc/XMKqojjv25/TqoD+zqCgy3UP8Bsp5cHTaVs0JkUAiBAnQvyV57O5\nCk08Qlb6yeHCI3K4XFlcXgOfmsQ2NIKlSfJZDc206LdGQOsjny8lYynoZ8g3+eKLeTo68uQSLroH\nLTTN5rL3u1i42sdBn2TnetjyjOMfYQRwC0g7tUywLRxvuBuyCunRHHd/N401X+OoT8cMwqIq8MQg\nZ0AqCwEPuMZJqcRiCrHYqT9MW5tBdbWOaUra2iZ2+Bd5GyFhaqLB7yyEEOuAb+DIXDUIIZbiZM6v\nm6xt0ZgUOSlRZnA5qzhoHcIlDHQrT8Q1SotdS0OgDU2aCCRJ4advTw26gH0izTL/MAHtRCf8yRgb\ng+FhqKqaWAvr97/PsXy5IBDIsHW7RSJts3pBkI9e5eE/jsFAJWzzgJ21nXJsQQ1yvlcrXh0vpaOC\nzOR4eZuBec8IH/liKaqAPSPw+FbwDYJLQIkLLpsPl88/uRz/ePJ5p99r1wZ44okEQsCNN4ZP3eht\nwMCAo2OmTvcVsGloTHCKaV1EQRBSSrlDCDGlRMaiMSkyIVery/mfrJ99uQQqJi7FJMgYlqWiizzD\nWoReUUpT5WHKR/uY5atnZbmFEGtwBjYTMzAAd98N6TTMng0f//jETuqXXs7w8M9HyOcFEOTLX0iw\n7t06t9eq7AyAxwfpnALSBlWBiBd0FySkc0OQClgjIHOYQtC9NQt5G8WjcHQPrG+GCgXok5QAQ0OQ\nzQuuv+Dk/Wlrh2/eA8daYd5M+NRHAnz+8x4UBTyxMbJYeJia5r2UcsLkynPBoUPw3e/CddfBlScp\n3zxtkEzLZS4ccd7R152TU4oZLxqTIhOioPIjb5QfWBs5mFeZM7KdLp+Lg+659CUqGBiO09zTRLY8\nyMKGh4kpL6KpVaQR5FlMhgRhyvBz4mi9u9sxJPX1zg0skwHfSXINL77Yxd9/uQvT6ON4Od7urkb+\n4R/S3H13kI8vhOGD8If1kPUpkMBJitdwQrNyFlhJII0AFM3CMDUUTdA3BFuOgZ6H3s2Skf02mRxs\n94PxaYVL5wlCr+tTNgv/++9g224npHY06eh8/c1faMRikMBGmcK1Z1mSX/4yyY4dOebOdfGhDwVx\nuc69UYlEoKHBmS1OZ7KWo5w9DdkrhLgVUIUQs4C7gI1TaVg0JkVOiSaC/LHaCS4Tq/cJHs4v4ODI\nAvpTZRg5N1W+btyjJrt651HuHcYfTnHEvY8UYyioCASLWIuP0GuOO2MGhMPQ2gpLloB3Aod1uMSF\nbacoOERwlq2OsWuXBgRZUA3L50FNGB5/GvoOO8mG5ADDALJgDyCEha5r4FKYfbmP/qTgme0wkAar\nGVx9NlkT1KBgdFDy0rOS/g8LNOA3T8H+A5JYFJYthV27Bfk8tLdBuATmz4TtO2HuLIjF4uQNm0Mt\neXQdZs/W0bQTjURzs8GWLVnq63V2786xZImbRYvOfb2RsjL4sz87170493gUmDWFn+MdGM31WeBv\nca6gB4Anga9OpWHRmBQ5NcIPno9B7lFU7zzKrRDpET/WoItQfIwyVxerIy8S8w6wLbGcH/Ws5KaZ\nrcz0ufARZoxBUoycYEwiEfjc55wcknh84iUurwc8Xo10UsNZd7AAHSltenpMaio0rl0KTwq4+UNw\ntBUGWxT8YyaJviRGZpT+fpuRERWfT2HWdQGWfaqErccgnwWyYGQdgcrj8wkFsCR4dHj4V7DxZcnz\nfzAZHRPMqJGYOY2BUYFlOXIxZh5+9hCUl4Jt29h2Att21kjmzdP56EcDqOprP+BxA5PNOu+q6xPP\nStJpm2PHLISApiatmA/yVjB9l7nmFx5a4XEjsA5HVuWUFI1JkclRG8B3F1TewIKO/4fYrTCiRDCF\nyjxrLzlb58XNq9nbtpAWo47B2X7ed/koq8uFo0L8OkNyHK934hnJcWbPEtx8awM//O8RbMtxqsfi\nVbzrXTrDwxYVFRr1NdBgwkgSbns3rKgBIVzkcnFaWiLkcpJgUBCPq7h9Ci+PCf78eRgaBK8BlgeU\noIIvYWOO2rjDgrXvEVRE4OBhGBqwyeckVRWOvyRWKgmnBBkbfCXQ3gKrLoDKSnjhBZPWVgW/X+Lx\nQCZjsGaNRV3day+1+nqNq6/2s3NnjjVrvEQiKqmUxO93DIVlOUtqtm3zve+l6O+3kFJQU6Nwxx2B\nokF5K5iexuTHwF8BezjNEISiMSkydbwNdGW+SHlvita6LoRl0dzRSHWsg+ajTfQbcRL5EK0HGvmB\ne5ALb1RYSCN+3rgwlaoK7v7PIJe8ayU/+EE/qgIXr3Tj9QoqKzX6M/C9A+BSQLrg8V5oKoWoB9xu\nhTlzTgwEWO6DVQGnIlEqAD0axOMCf6NCOguxBojNFNyzEZISXG7n5t7XD+Gg5OO3S55b71RlnFEN\nC+t5paa9bUt6e2HZMscPdOyY5GQ1g4QQrFnjY/VqL/ffn+PZZ3Mkk5KFi+H+n6js2K4hVEHTLJOF\ns22uuMy5VA8fNvnVYyZ1dTqzm5yoq7crluVExE02oDgnTNPQYKBfSvnoG2lYNCZFTotkNgg5nfDT\nJtE1LzCcivBYzzo6h2eQln5cgSyJsRB+fZikJQmrpW/6Pd1uwSdv93DrzdVs3pwllbJZtsxDJKKy\nY8Bxlgo3mBKSGehMO8bEtJwsd9frwo41BaJex+hIoL8K4j6YVyVY3wJSdRSEN2+C3UcgP6JiRSVK\nzuZv/wpu+oDKygucMr8rlsJAP9x3n7NkV1mpEY+n6O62yeclS5fq1NRMfJkdO2aza5fFli0qR49a\n3HOfglR0J18mC7s323S2SxoboKoSdu13MZQSlJc7mfuf+RREIpIjRyTZLDQ0CILBMztrMU2bp58e\nwuNRuOKKkjMSfdbT8+p3dtFFsG7d+SU5k7Xg8CnESd/B/H1BTuV3OH4TAMbVrp+QojEpMmVM02bH\ntnZ27tRx6TqHn13InBt2M5ozEANVCGljZHRylpt8VCMvDgCrztj7ezwKl1762vAqXYWdFsiMczNK\nmPA+C7Y1w682O8nwaxfC2kWvtnHrsG45PLIVFOFUjPzk5dA5BkoX1BfSZIZ6QS2HA1kINmpoJjy4\nHq6/HlavfPV4oSD8xV84IcXxuEpra4iHHsoRiQg++UnPSR3wx9E06OwUdHXZWApIoUNEOKNiHyA1\nkmM5nvuDyeJFKnlTYcliBY8b2jpg115IjFhs2CBRFEkkIrjzTo1A4MzdmTs6cjzzzBCqCosXB4jF\nJq/zMRm/+Q3s2O8kim7fazJzpsKCBeePCq9HgVlTmDG9Ax3wnwDm4kS6HJ+bSaBoTIqcOX7zmw46\n2ga4+vIS9hwKIAer2PuHAFWX7mXu2n20tNfTN1iOFQE7pFDB6RXwsCzJpk0mbW028+apLF48+ekp\ndQj4nDBjCcwNwcsJSLwMbhcc6YWfvgSzq6BmXC7lhY0wIwZDGagvAZ8bXm53nO7gVNqz3gXtB0Em\nIRsBIw1Pl8NHfwdfvhiWVTjGCJyAgkhhNW/RIo1Fi6Z2aTU0KCxZorBxo41lK6AWlsQsnLooLgXN\nkoTjXi67TOAN6Xjcr950LUvy0kuS+npQFIWWFptjxySLFp05Y1JV5ebii8N4vQolJRNkl54mxzqg\nqxdcWpZ9Bw2+8R8G934rgst1nhiU6bvMdaGUcs4baVg0JkWmxMhIjpdf7qe21o+iGFSVO2sAnSkX\nj+59F11WgjE7ggwKPLNTzCwbZpG4/DXHsE2To089Re/u3fjLy5m7bh3eccXeN2wwefzxPOGwYPt2\nC7cb5sw59SlqAHPCTulchHNz70w6EVndCWgbgbAOmfxr2x1JwAPtTonhuhTcVufkOW5NgceCXg0y\nEQOx0MIaUcj0qGALTD881qywvgX+ciV8YdWrQo4H2mDjHmdZbe0yqIpzApYl6ejIkMtZhMM65eUe\n/vqv3QwMpnl2g2Csw8JEvFrvOG/gjSR4340RPvgBQToHR446L0UisHwJvLTBMaY+n0RKgcdzer/t\nZLhcCjfdVD75jqfB3HmwZSf0dlvESmySSZtcTuJ685OeM8f0dMBvFELMl1LuO92GRWNSZErs2jWM\noggU5bUj3jKfgT3fwOXLsLi0GcPScJPh/xhx3OK16wRt69fT+vzzBKurGWtrY+f997Pys599ZQ2+\nudkmHlcIhwX5vE17u82cCcZIh8bgWArqAo7hyAFeAZ05eFcUeksh0QaVfphfBTUFmyWRDFt5ftCq\nENVVKjSF1hQ80Q17E5AGxvLQ7MqR0Q1GciqKG7QGR4/c6tOxbJ3RmMp/bHfy/O+8AJ7bDf/1OKCA\nT4cXD8FXPgKxcfma+/aN8ctfdpFIWGiaU+elrs7LdTdUc/MnfPQoFq5m6G6HxLCNkkvjLRvlggtj\nfPBGgabBx26BI82OunFDnaP8e8stCj/5ic3QEFxyiWDmzPPI+TABV1wCh5shkfTQ3ZXnkx9xEQye\nR/ot0zc0eBWwQwhxDOeyEoCUUhZDg4ucGTo70/j9J54uPbbOftPLEu9BREpQHh1gqCXKnoodLK2M\nE6AWE5vBwZfp33YXtaUDpDzzoOJaUm19mJkMeiH1fdYshf37TbJZQTYrqa8/+c1lJA//0+Jc71VJ\n+GQNPDoICQsuDcN7SsC+HPZ3OD6TOVXgdUMbw+yhmx47zz67HN020ZMhgmaQfaMKWRuunQVPdFuI\nwBBqVqK4g7guyWGrCtISaLaJMmZhDHgZNBS+dQh2toDZ70R0RYOQM2F7FzywEf7sGmcWsWfPGF/9\nahtDQ24M4SdWKSmvV9nSrPHf/5FiVr3KmE/jksvho++GrdugrTPEwrkhrr0SwoXoal2Hea8zsI2N\nKl/6koJlnTxfRUpoG4X2MSd/Ju6FmVFwn8Orv7oS7vo09A2olMW9RM/HqLTpucx1zRttWDQmRaaE\nooiT1kIfQyF/0M+oXUJFYzdmTqe/q5zEwjYsciQxeKjvGVJ77+OPZvTgS6WpyL3InlQOd/R9aOPW\nZC6+WMPthq4um9mzVWbOPLkxUQRoAtIWuBVo8MJdNc5rLRxmC500KnNY2lD5SpvD9LOJVkJ4CVg+\nunKCvlEX/mA3uqeFVm+WrBLGp4cZLUuzJLKLTF5nd2YByf4IUTGEcEEyHSAbcKNqBlaLm94ReNSC\nm6KQGHOkwbwuCLlgbxcc7QUtL/n7f+jlSFuIgXSU0aRGtlnB6lNwlUl88SxjZp5FtQrSpeCNwKc/\ndvq/j3ISd0PbKPxiP/SlHJV+RThRb7oKVzXCxTXnLooqWsL5aUSArAGH+891L956pJStb7Rt0ZgU\nmRK1tX527RoiFnutxkQMEyQc3TqTka4YMg+x2ABzYhEC1NFMikPN61nZ0YzHSGGoLjwiRalviJKb\nPoAYdwdUTIMVMwZgjh+CE6vvhnS4o0myLZPCCg3yS/JUE6CRAM0cxoOXI+yjHMeYZDHYRjsx/KTz\nKk/1QTDeQ8jdj+LKobkyuN1ZAorFyECMuJLHTKsM2DEs4cJHEtPWEEi8aoZs0o3ABhOMHBgK/K4f\nFrlgaMyZMTVWQVkU/u1nMHQ0z+8P66STcfLDCrbiyLqwH/I9Jvmgh5FdCgdVmFslcSmCf7nVMUyb\nNsGOXRAJw3veDdHTiGloG4V7t0HYBfWvS/XJW/Crg5Ax4copacJOLzwqzJqCVuc7MJrrDVM0JkWm\nxKJFJTz+eAemaaONK+heoZpEPRmGcj76OsrBZWPWKASq3Qg0IriRITe9hBEIdGGBS0dfcjGh0ir6\n6SE7sJ7gCz8j33KMsazO0ZdGSebKiV50KcvvuAMt6CU11EcwWok34gxlu309DA5vJ9oxii8c49CM\nKg6qQzQQIssotTQCzrJOJ6PYAjRUdo0A2gihknZ0dw6Ega1IVNsinhpkLBEhEhlCuGE4EUIRFp5A\njsyYn+MhPlIR2EMq6DbknO9iUEDCC5dWO7OStjQ8fRT2t0DIpzFWX4a5zw1RAQVXkpAmeqOBrzqF\nmdJItkbYfwTSBnxqDYx1wy8egdJSJy+jvR0++hF4+mknWfHqq53Q4oEBk02bspIPxecAACAASURB\nVGQyNnPmuFi40IMt4Wf7HEMSPolD3qVCXRh+1wwLS6E8cHbOmz/8IcmuXRk+8YkogcB55BOZjOnr\nM3nDFI1JkSkRCOisWVPB0093UVf3Wq2pr9Z18pPeCC1VGUpcSf72OoshNc8YWUrxcfPcG3gykmDv\n9hEahzpQEl5igwvZ2XUfe40N+FPDJFd4MWbVkDuWZvSiGownWzEf+DYbfvCfVH7uIrQrZ6F0ubms\n4WP4qmfxxP4Hmf/c73GnkihZFd+CBrZedTHtgWrew0JKrGr+a9TgaXsMQx+j0a2wMifoTRuY/h7G\n0j5IugmERrE0hXzOxZCIYGYtBpo9BEpMEoMhMj4fOcNDJDACQjCYiCNUsE0BbglZ5ztQFdhrABlw\nJ0HNwHACUCCtC8yo21mD9zvbsEGWKOSrPeQNN/6FSRgDxqB9AF7cCYc2wpEjjuzLzJkwMgpPPgn7\n9zvJmLNnQ0mJyd13D2OaEpdLsGlThhtusKle4GMwfeKMZDya4ix3bemC62efnfNmZMRiYMDCMKak\nYn5+MT19Jm+YojEpMmXWrKkkl7NZv74Xj0ehtNSDqgqMnMXlRicXlQwzZ40bX8yPgYKnUId9ubKc\nuVWzSJrXEnrsYVw1c0ge2cGhqgzCsAmmkrTHq1D9Y3S8ax4pfwR71YX46jdg/2gHh9uSZMoiBJoU\nOpNPoMkOSHVxcGUDmi2ZtecQ2rE2dmSvZa81g7sNixJ7Px5VYGQ8RAe6eFH38VK3m1S7F9eseqqr\nW5DSpDVRR7hkDNVlkWpTMHZmUKQO0RSzklsZqy1h2FtCf7oU21QRLomdBaSAPE5ql+n4IoSEgAIH\nklDuh/ZKsGsg1alA2oY6YACnnQeoVxwD4pZkUx4nHC0hsDW47yGIpiCZcDL52ztg1mxobISDBx25\n/lgMXn45i2lKqqud7zoSkTz1VJLLK73oyuTOkJgX9g+cPWNyww0hrr46iMdz6vwR24ZnnoWt26Gu\nFt53w8lLErxlFGcmp03RmBSZMooiuO66GhYvLmHz5gF27Romn7cIh11cc001cxbPoTXURwaDuVS+\nYkwAfATxeeaAGoGhIfr8NllVJ8wwqtciag7THS8lpUUI5kYwNRe56xaQGdMI9/XjT4+RL4+Rzoyx\nLztEbS5MlzYDzZ8nV6nhyuXY6lmEhYZHyZCUkqQiWJDZQXl+kNpRwcsVqygb6cWdz1Hi6aVFrWe5\nsQmZUukaq0N1+9ntfxf5vE4k0UvV4H4aPG3ES4fpooIxGcbI6EhbBUU6Rbc00C0wUlAaBNV2wotb\n3aBmwe+CwRKcKy2MM9pVCw8LR1VfEVijKmSFs58F/R3w/nWw8SVIJiGTg1gULrkE5s4FjwcCAUdR\neHwdFF131Ixz5tQc64oA4yyOwBVF4PFM3pH9B+CZ5xyts737IOCHde89e/2aEsWZyWlRNCZFTpua\nGj81NX7e//66EyoFlnIKb25ZDVz7cehpQcQvRc1+HxUTy1LIutyoLoGNiqVqaNIgJXQU00Zr8KKW\nuDBMmyOHQljzTNpLa6i0esjpHkb8Yba8fzlRfZAGWgiqCSTQY1eS8PtAtTHTOmpJCvNyieqSSCzq\nn3uWeS89yW/Wfh4jLJG2TSCYoi9bxqBRAyGTFleEmf3NzNNb6BuZz4j0IEwFTRdEKhSiOqwsg/YM\n9A5COpcnGE0xs1TSqbjoTnhwqwpmsDA7OX7FKcAgTqKKCWRViFmg/P/tnXmUHVd95z+3lvfq7Uvv\ni3qR1Fosydola7G8gTHYZg3GhgFDwHYCY0iGkwCBmWRImAkZMpAEQsIAYQlgc9hsNtsY431fsKy9\nW73vy+u3L/Wq6s4f1bZsWZbclmSpcX3OqXPeq3er6nf79bvfuvX73d9PJWJBQ8LN/r/rApichEOT\nMF2Ge54CzQ9rOtzTLF/u49FHy8TjEl0XDA1V6ery0RQVVEdO/F3mTWg8Tf6S+ZAvgK6B3++mp5mZ\nObP2lKvQPXpmbVhoeGLicVLMO+lf2zJoW0YzNoVsGio/IhWIE5MztGYPkY7EyPujSNvBypToXOVj\nbNdmDKOKM5SjP7aZVdlnMCMxMuUIqiOhWSEWz5BU0+iiSokQEoipWZoCw/RqSzBr/AjbYkbUIIXA\nt3+UlXc+ht1Rg67p+AIOZVGlzddHdiRMORYgn2xgfe1jxIfGMQ+Uac9nWaH6qVm+mkAsyfLYOG9b\n8iR1+iB5mWE83wfVAj+cuZADSpym1gzpaoThfDOjsRYmf95MNe6HnHAFxMGdxigOxGzUdovEbIU1\nwTh17XCT6rbpe8phZhRUU3DbE4LLr4QHGyXnt0nGR3W6usIMDORxHOjs9LFyZYjpAza5CYEZEvie\nlxusUHCYnHIH7doaQbYieNuKU/f/8ErpWgKhEAwMue/f+uYza4+hQtfLiJzzormO4ImJxxkhgMof\nRa/kNhYT5n50y6KmOM3b07/ggL8TKxNg82QXMt7O7YUZ0sEYVimAUwnQOtuDZpjkZQeVmB9sh7ry\nFNOBOopqEKSkip+AXSTmy7JJf5w8EVJqkv2VZSBUcqUQSnuQwW1rsGsCoKo4VQ0jWqJNHwAknaHD\nvCHzCxKhGWomunnKWc1j77qCxyIOO/Wn0fX9PFNN4ZQtsopGPhgGqaKFD0JhNdPFKIZi0hCeIHBO\ngZrEJAd+fi6m6ofZuWpc6pwvoUXBVnWspQWmVZtol8p4CqYzDnksqFOwioKhQYWevYKZIcnAfZLG\nOsjl/FxwgZ+dOwX//u8OX/6yg+PYDGUljywSnLcNklGH237j0NurYBgqPp8g1ACv267QGRfs3Vsg\nn7c555wQkcixhwXHkS/KgHCqSCbhv/4JjI5BIg4NDTA97abzb2hwSwS86niPueaFJyYeZ4wG/LyP\n9VRYi09TUBoE0v4YG50iIroI5gpKLaHEDDmcljID9/6OVFVn/aLDFOsUnnI2MBBuY4P1MFquyHi0\nA0voRO0sbaIfHZsIWRx0TFGgSZ0gJyOsqjnIiu1FpltnmXFSGPkS6VKUQ8HlaHUVbKExqrRwT2QX\nxtQUpddfCo1+RNQhVM3z5J1JHtXfRHKLYEXrXpL+GQpWkFGliSAmreU+0jJOQYRo1kf4fXktxBUa\ndw0x+LvFgAJVATnHHbTGBfghNxPm4LTCrISVW8A3adPjl4iYhW3pWBNugS4rIlm/GSJhQU2N5OGH\nYWpa8k9fdhNGStumUnYINNmM7p0in65SDdUQqQ0yW3bQHJVCr2Rg2uKH1TzPPDODosDDD/v5yEda\nXxD+DXDLLdMcOlTkIx9pIRg8PSG+0ai7AYyNSf7t32wsS6Bpkj/5E5WmpldRUDwH/LzxxMTjjCIQ\nGBwZnITaAEeNVQECtBKAJHzg0iB/+8gbqStnseJxnlFXgeUwo9ZR40ySlwWCdpEEs+iiisBNfqiL\nCiCxhQoCFteOUj1k0trQx4TaysRkiHjvHmqbJEoOnIZaBowOnpjaSSXvp3nRCBE7hT5Vxqn1k7tk\nBY2VMYKVPA8PnY8asGiuH0Kagp6JRQQzBYyqSTEYYr9+DhOHmsg+mXCXoRvCdbY/AxQ4MnBNCOxN\nOrYtmc45UIANHTBTmsWaVskNRwkqPi7ZCQ0CrCqk09Dd7SZ6/MrXJWlLQQm4pYQp2aT3ValtKVCW\nKlqwiu1XsGYk1SkLy5I8qlaRlRxbt+rEYhoDA269mFjshWLiOBLblsfMgnA6OHRI4jiC9nbB0JD7\n/lUVE/DEZJ6cNjERQhjAvbjxKhrwIynlXz/v848DXwDqpJTTp8sOjz8s1vtDXF9zH18cfRvqMzYT\n5zTg10z2iPVcbvwCn13GFgoV/Fgo1DJNRfhxUEhYsyQnx4n4iiyODZI3CkTyFtsyP+epW0JYT88w\nfIEPWZKUWsukLt2EOiwJiDyZUgJ/ooIsSEaHFuNvKLB/eg2GalETmURGNPrzi+n096BXTKYiDWT6\nEwSqRaoFlYlftuA8m15YcWchtAAHeG4RIw4QEXCOpGxJHtybY11XlXdsCbP/6Qr+vMbrb6hy2S6T\nRCLEP/yDw5NPSnRdovkU0mUQEbfuDLYDcR1SgnwqTCSs0VIOoPSazMw6pGydUFhBMR0mJnykUnlS\nqSpLlgSOmXDxrW+txXF4US3700VDg8A0JdPTEtN037+alE3oHn5VL7ngOZ0zkwpwsZQyL4TQgfuF\nEL+WUj4shFgEXAoMnsbre/wBElHXsSP2c/YH9vM9/U1krASaalNwItxSupIL1bup0VMIHaQicUyB\nJi0cS7D+wd+wePduJh2D2IUxRu0IAbtIcChP62Np8ocqdBX89L/nCsJGjsTwCDOlDhwtRjalMp5r\nIlDNIxUFv+3H1lQKhQDx+Cw+pYKZCzBweCnZ6SiVqI9wU4bZ/fUUDhlgg/ABFZAVt4oiSVwBqQJh\nCbUO5FX3kZcuKBgRDu8ZYsfSET6zfT3bb3RdLKWSn0RC0NSkcPHFkukZ+OkdDjIocEoSoYCUwr2z\n1sGv+Vm+PEo06iORgN7eEpFCBU1TKFqS1tYgH/5wgmLRprMzcEy/iBAC9VVcwL5iheCqqwQ9PZKu\nLsGKFa+umBgadL2MIqGeA/4Ip01MpFv4Oj/3Vp/bnp0kfxH4S+CW03V9jz9M/DSTbLiGSwe+wyPB\nUbodP1OFBjLEGXeaOEAXa5IHaCsNEJzJkmSapsA4m5+4H/mrwzy5V5KVCrH7pqhbnSFVCDJx1yzV\nGRuZhZrICK3//s9oLQbVd9UzYq1EhCWOpsCMpOrzoxkm4dosqqahpB2mK3UkrBmcrEoxE0QisEsq\nwgbDKJGxw9hVFWELpMCtoGjg3m4ZQAL0eBlfjUmlbGBpfncxoyYYP7iIr94R4gvdFosSkr/+tOC9\nV+vYNhQKgtFxh5/cJSkYClJ1j5FVB4GKVCTRmMXlF0fYud3H974nMQxBfb2P9KEiuYxNJALve5/B\nokX+4/zVzwwbNyps3HgGDfAec82L0+ozEUKowBPAUuArUspHhBBvAUaklE+filrSHq89YsYutnYt\n5iPjt/N1v2RcS5N3QhS0CAFdZVO1hiv+780E6msY6etl9YWPInMF9gwqSFvSvtJhfCrM7m/maVkD\n1QEb6QMRgFJJJ6CbVBWd4fQyaAAnoSKkxPGp2AUbtcmhoX6SsJohm4gR1nKouoOpG2TNGP5AGSuq\nUhgPE4gVMRaXMIeCWGUFIjqK30KaAukIWOUufJSqQtX0ozQ6c1UWAUvAqCA/FEUGbAaGJH/6Fxq/\nvavMR67TaWwUfOsmSdaaSwVsibkFkQpUoT4Bf3pVhI9crxCNShTF4uabwTRVYrEQK1Y4fOhDKm98\n49lRkWp/N4xPwoXbz4J68Kew0qIQ4pvAFcCklHL13L4kcDPQAfQDV0kpZ+c++xTwQdz/hI9KKW+f\n278R+Bbug9FfAR+TUkohhB/4DrARd/XSu6SU/XPHXAt8Zs6Uv5NSfvvU9OrFnFYxkVLawDohRBz4\nqRDiXOCvcB9xHRchxPXA9QBtbW2n00yPBYihtPK25g+yS9rcY+fpxySBYJsaZEmpkfvVJgxfE4ta\nazn0yzwtrxtgyfvrEU6SurXnUp41+N1ffJdiuEooISiZkqqqI4sV9sfWs6/hXaSinYRiOcp2iGrZ\nD1EgYaHpVS53bmV5sJvRQBM/sd9BZ+AgZsxPqStIuRQgHwigNMHQE+3k9iZRay1QBcKy0IRNsKZA\nbiJCJWAACpbjA+lAQHXXoJjAHglZkNM6hByQFpUyPPIwHN5vsmGjxoTPQqlXkVIgbdWd7Uy7qfmv\n2C6olgXlMtTVCW64Qefd73bo7YVgUKWtTeD3H3/UHh+HRx5xw3O3bOGYae5fKb29WWprDaJRV8ye\n3gvdfbBzi1u35Yxz6mYm3wK+jDvgP8sngd9KKf9eCPHJufefEEKcA1wNrAKagTuFEMvmxtKvAtcB\nj+CKyWXAr3GFZ1ZKuVQIcTXweeBdc4L118AmXHl8Qghx67Oidap5VaK5pJRpIcTvgLcAncCzs5JW\n4EkhxBYp5fhRx3wN+BrApk2bFmCWOI9Xgxqh8nbtqHT1QWg7/3z67rqLWFsbreuvwJlJodT0428Z\nJbK4lrihs/3T1/LUTfeSXXwQY7RAZUZFubADpbEOqxKBQYeqDFJN6hCTbj1fC5YmeqjUBXjc2USo\nmuV14k5+OP1O/vuez5GYTOMrmOxvXcGXon/G7DN1KAGLcjqIFjJpP68Xq6ChKIKgVWAs1Uy1akA9\nEFNdx/yghBkJKQExAeuAkoB+BSpQiSkUsPm9lia83SG0bwhp2swoyzBFDOIOsizYMyCoKPB//g22\nb4ELzoNoWCGdhqYmd+Hi8bAs+I//ANN0o8VCIViz5tR9d/ffP8HatUnWrq0B4O1vArN6lgjJKQwN\nllLeK4ToOGr3W4AL515/G7gb+MTc/puklBWgTwjRA2wRQvQDUSnlwwBCiO8Ab8UVk7cAfzN3rh8B\nXxbuAPsG4DdSytTcMb/BFaAfnJqevZDTGc1VB1TnhCQAvB74vJSy/nlt+oFNXjSXx6mm85JLQFEY\nvPdepJTgOITstbTv/AyRYC2gsvbKIHXnPMGdP/kYtcl+ev5ihMb0XuTyCG/IfZUHwj6KkXqGrXZm\ns0mcgCRgFInGslCVlOwAWSVKoxxDzTjcHbsEJeywfeY+uqZ7CJtFpqSKP2RimRo+X5UVG/aRytTg\njOv4gyVmxmqpKj5I4IYM+4C2ucdVFQElB4QCLQrEFZh1qKwCp1lntjZH4OEKUXOIEW0dxCWKzyKg\nF4mYGZ4abuTRvRpYgn/5jpuuZPsqhfogXPlmN5398bBtV0Tq6qBchkJhft9Bb68btnzRRRyztvt7\n3rMEVT0y1fH5jt3uTFA2obv/ZTWtFUI8/rz3X5u7ET4RDVLKsbnX40DD3OsW4OHntRue21ede330\n/mePGQKQUlpCiAxQ8/z9xzjmlHM6ZyZNwLfn/CYK8EMp5S9O4/U8PJ5DUVWWvO51tO3YQXFqCtXv\nJ1Rf/6L0L3LGJu7UoSTHKPsFjWaGTtnPrB5hp/4z9sor2OJ/nNvLlzGaaySychZT0ygUwySUNDNa\nDbvNc1mT2EtcmyUfCHFYLqF5fIREcoru4HJkVkMVksCKHGktRsKYxTFUKiUDO6pBVLgLGCXu4y0/\n4BMQUNx0KzruUJIAkVRoXimItilMlGykArZPxUzEwFbxGWUcn4KjKkSSafLlCGZOB0dQLUrueVKy\nZhF8rPPEoVl+P7zlLfDrX8Py5XDuCauAv5B774XHH3eP7eh48efPF5KzDUOHrqYTt3sUpqWUm07m\nWnN+jwX/9OV0RnPtBtafoE3H6bq+hweAHggQO47PLXX4MP7xNvzp27B1HVtxCA1P0iJGaN82SdHY\nQzkYpNAaYG98BWZYx9FUHghuQy/b5CthIuRo0MfJECVazJIPRJhorKOy2k9rQx/FwQhxfZZKrY+n\np9YReqqMyCukRpNUMgHXF+PHFQyJOzuRuCmIn91fAurcATjUAETBp8eQHWWyhU50WUKNqPiTFpVZ\nH9nxOAFZomHLCGP3NmOlfe45hcO+Abj9dsGOHScezDdtcrdXwpVXwubNcDyX58SEzdiYTTAo6OzU\njlnD/oxw+lfATwghmqSUY0KIJmBybv8IsOh57Vrn9o3MvT56//OPGRZCPJufemZu/4VHHXP3qe3G\nEc7eWwMPj1cBVdcJJGs5+OMytYbJVI9D8844y96UoKonCRZLPKGtJaeG2RR9nA3VR3BsQUkEmNTr\nGCs249NMwkaByYY6sjVhhqMNlNZovL/yDd4W+xHJnZOEz8mTNWMU7osw3t/CWKaZij/gRgw9+yt8\ndnmvI92V8ebcfnuujSVINEja2mwiEYfWZB1IlVJdPQV/PTKkE0lmCDfkiTRnqN01ifA7+GvLELZA\nt6BqY5tw110Os6fIDWtZkmzWxrZfeHNdUwOrVr200/43vynz6c/k+NANZd77vjJ//Td5MpmzKCGW\n8zK2V86twLVzr6/lyDKJW4GrhRB+IUQn0AU8OvdILCuEOG/OH/K+o4559lx/BNw1tzTjduBSIURC\nCJHADXy6/aSsPg5eOhWP1zT1q1cz9uSTNK17K6kDP6OScxh8rIJxeQyBwmiimYFIK32D7dQ4Q7Sp\nh6lvHedg6FxyIoy/UiE3DFOL4tTqKSYLCS6qu59wsIqQJh32AG+fvInvJ9+PmDJhUnerLeYEhB1o\nF1AQEAHK0t2nSwgKV0D6FDfkV1dQHMmKVWW6VYvZio8kOn92hUK4+5/4+e/P5ynOBT8ErRyOiCNU\nMCcNlICDbpiuo1+xwILeXpXvfMdm40bBunWCcHj+MwIpJQ89VObOO4uYpiQUElx2WYj1649RJ/go\nhoctvv+DMrfdrjGbMlCEZHy8Snt7mRuuP5NVseY4hTMTIcQPcGcItUKIYdwIq78HfiiE+CAwAFwF\nIKXcK4T4IbAPN67vI3ORXAAf5kho8K/nNoBvAN+dc9ancKPBkFKmhBB/Czw21+6zzzrjTweemHi8\npkkuXUq4qQnN78cpFylM3kH9vlEGmoIU3ryMMb2NHtnJaGYRB7V1LOvch+GkWK4eJEeUhsw+etQ1\n9E11kJ7x8V8CP6C5NktsIkcl4EPGBIWIgVURqDkLEVUhDcnqNNFyhuHlrTj7JdqEg50UOJpAVSSy\nBWRRxdEcGFVQbHjf6yVL3zxF2DHYnS9ysT/Gu6IF0k0ZppVJup/QaF02gl1QKI1EEIqNGrWwh1Vs\nUwPTdoXJEciQpFSC226TPPSQ5LrrFOLx+QnK009XuOWWPK2tOn6/oFRyuPnmPJGIwtKlx/ek9/VZ\ndHc7VEwVXZc4DhSKKg88UOaG60/mGz2FnKJJkpTympf46JKXaP854HPH2P84sPoY+8vAO1/iXN8E\nvvmyjT0JPDHxeE2jaBrrrr2WPTffjFXZyNIlK8n3HKSmb4ZFT5hsvKqF6+LtGMvqmP7O53m0roDZ\nVCJhlUkfMKm9Z5IV0z8lWCoQH02z7S8tJtNLCFWL+IsmxUCAqqWx9Knd9OxZDzEHFEFbtRfFnCRd\n9aMu9rHNfz+9bYsZEO3YAxp+YaH5TapLNYJPOVy92cfVF8f5rabhGEU6NR+RUZXP/24J0v4cHW2S\nN9l+BmfWUowPEF+SYeihVsyUTnnMQOYUQIIExbG54AKdZFKQTMLwsOSXv3RYv16lrc2t4PhyuPvu\nEg0N2nNrVQIBhXhc8sADpeOKieNI9uzJMT6WQRFBVC2BrEqSNTaR6NnhMymXofvQmbZiYeGJicdr\nHn80ysbrriM7MkJ+fBwhBLG2NoK1tUcahaHmQ18g+bPvcfDQ/fQvT2MdnMB/dzd058k2LmXVVgd/\nwCGez5CKJVAsh5LjZ8kXn2TbwD18ceVXGLcsKgmdxP6HieQPEsntxbpyE2GngPA76DVVjJiJMVZA\nSStEmy1WdRwm6Kvn+6MbGVYaiOk2pFRuf1Dl0g6IBuIcHoTrdsHYZJCHdjdwxVvgzjr46S9s9kzY\nmKpAFBw0S9K2WLJt25FBOxaDb3wDtm51w4BvvPHlrfXIZBzq6l4YFWYYgtnZ49/SP/VUgUOH8rS0\nCFLpLJIsug/q6mp55zsi8/nqThuGD7pexlppLzfXETwx8fAAptjDTMs+6lvWkWTZMdsoPh8NV32A\nBj7ADiwOr+jn1uy/onz3JzS1RAm2aaj2MDXpMWLZFLnGMNM5QZwhGjbbfPC8h8kc3MHToozT6Mc/\nWqLaZDBU105OxshEwkTUElWhYyk2S1pmyOKnoApyZghTgSmfStVWKVluYmBVg4DPzb4SD4LRAL0J\nWNoGW1bDR98t+NznTKQU5POCZ54RJJOCvj6HoaEqy5er+HwqjgPt7TAw4K4nicdP/DdbudLH/v0m\nTU1HhpHpaZtduwLHOQp2764wNaVxySUqgYBkZkZSX2/wsY+F2bXrLFlo4tUzmTeemHi85nGwmeAJ\nfISY4DGSlVY4+CBUTVi2FSI1LzpGRWNZcCkfv+EfGbv+7+l74pMcvL0XfcImsXiW2UiUlIgTT6WJ\nfThMeVSl3ujmqjU6nfF9PHDZDg49so22FVPsStyNXzEZpZHuygoqShJL6tiKhoVCSasjItq5eBH8\nyzRMVSARhM4mKGbdVWmRIDTEIdAAXe1wdwEGZ2FXUOHaa/3cdFMFVXUwDI2mJo17762Sy9ns3l3l\n4osNtm7VGByErVslAwNFDhyw2bQpgqa99GOnSy4J0tNjMjhYJRhUKBQckkmFbduOLyY9PRr79iko\nSpUNGzSuuaaOlSuDx73WGeEsCixbCHhi4vGaR0ElwRJm6aaWNXDv92GyDxQNBp+Byz8GvmNHKAkE\nzcJH8+rL6Z/eS/qhHyI1gVkvaIhPo+oWmvDRWJzg4eAank6meajej1m0UeMGWxc9jiHLWJZGg2+c\nlsgw9+oX4UtWKFQlimIQK0m2F3WmS5A0ocMPUQd8a2Gz5S6W37QUAnPpUfZX4PYCJFX4bgY+uUzn\nU5/SKBQkqRT84z/aTE8LFEUhm5U88YTJddfB1Vfr9PYW+OhHn6Zatfn4x1fyjne8dB72mhqVG29M\nsHt3hbExi9ZWjXPP9RMMHn/FwdvfHqC2VmHtWli+3EdLy9mXsdibmcwfT0w8PIAWdtLIFlRbgcmf\nQl27+8H0EBQzLykmz+HfQSmsMrJ9MfmR37N07DEuqn0If8BEqTg8KrfzuPZH/K5+nFR3mOJwhFg5\nQ39PJ3qjhSIdFvkGaCmP0jg5QW9hCZaq0RGdpDk5xm+LRZTZzWwOazxbn2qoCOtXQP1RpllAtQq9\nQyBD4NRC0O8mdUwmYecFblLF6YksTU1VVq4M09PjMDUlmZ01KZWqOI5kZqb83DkPHy4xMWHS3m68\nYPCPRBR27Dj+TORoNm70sXHjWfI46yUoVzwH/HzxxMTDYw4Vn3ub37wM/azbhgAADPVJREFURg6A\nokIo4W4nQhgEI9uZNkdpalbZvTvKPbmLMZQKqhmncc0V3Jk0mdkfJn1/I05eEKnN8njDeUgVQvE8\nh1LLWWP+HqekUMoF0XwmuYyf/kIzHc2HyWfaiFcbqfepmHOPYILH+AUrozB5DxzIw9ogmK0QfJ5f\ne9tWBZ9PYWZK41+/kiccDrBhg59EwmF0VOO66zrJ5SyuuaYRKWH37jw/+MEEigLFosONN7awZMlZ\nsBbkNGL4oWvpids9+sjpt2Wh4ImJh8fR7HgX9D0J1Qp0bgD95d1Ft0Z97J/uIBnrYOm211NKpZBS\nEkjEGQoKskoP6YcWQ9lBTUpKjUEC0RJ2WSPgKzFRbARlHYWZEIvMQZZGD+OvLzKVaydVSNJWk2V6\nKkk6G6AnD2tjkDEh/Lxf8d7D8P1fQr0JfXsgfA6Ejpq5LO6EjnbJ4GCAzg6VDRsM6uo0xserDA9b\nvPe9izAMhWoVvv51uOMOqK31kUplGRoq8YUvWHzuc0tJJs+G9L4eZwuemHh4HI3PgOXb533YuY1w\n+2E3G4qiaYTqn0uQTVotUbRUREFCo6StrY/zW+4jGCoyLhrZ66zCtP2MWc20tozyx6Gvky+EeSy3\nkaWNz9CfX8nhvEqbkSZeVVgW1vErCt/qg79cCfqcm+JAn+uMX9wCzXVupuBjhfn29VX56ldned/7\nYtTVucNAY6POu9+dfK7N7CwcPgy2LfjpT3VM009Tk1sSuK+v9BoQE88DPx88MfHwOEXEA7CqHvpm\noS70ws8MR0ERAmNxEZ9e5b1N32W9spty1cDRFXS1yr3KBaxM7CdKhqecdbxJ+xVBpcj9lW0EjRmm\nEjGGlSmemIYVUZuEpmGnI9z0TIjz26Aj4QrIY3shHIR0Dpa2wMFuWNIJ2vN+7S0tGu98Z4TFi196\n1lVbC+vWOdx3n01dncLsrE4qpePzmdTXn3i2lss5pNMOLS3qMevKn914Hvj54omJh8cpZPsieGYC\nEgHQnhfU1FH1EXIk+e1FFmcPsSX4ONNODbahEaLANvEA47E6wjKPY6qousUhfQmLtUGUskNOhuge\nXE57Sz++eI7eQoCoKgmos3ztgM5Nh3z8vzfB5lWQycPeXljWBod2w/7fwwXb4fI3HLHHMBS2bj2+\n3+NLXypx8w8rHOypYFkafi1EIm7xwQ/WsmjR8QMSymXJV76SZ3bW4fLLA+zadRZGbJ0QT0zmgycm\nHh6nkI4EvH4J3NED7XG3HDuAgWBLPsRtUYtEPIOGhR8T2zQJyBzCX4dqO2imTUAt0aiOEaxkGPA3\nU29O8HRlA44jmMkkSSYztNfmSRpQSRkM720mloBnUvCGRXDZDncbG4cDvwdNhXR2fv1Ipx1+/GOT\nwbRGpjkElnRDi4dMvvRjH5v74d2XQPwlUq9Uq5J83s25dVZlAn6ZlMsO3d3miRt6PIcnJh4ep5gL\nO6Biwd390BIFY+5XtrHQQE7bQ6A4zUNNm9mafRitWKZk+dijLyU7EMeqGrSGhik0hMmmu+i32znQ\ns4KJlkYIg+1oyGqA5oiPqk+yKWLQulgh5IPlR61ab2qEd1wJ4xOwc9v8+hAKQSIpGC/5ITRXWz4G\nOCq//XmFlnadR/fDpZuPfXwkovD+9wcZG7PP+jDgY2EY0NV14pnJo14+lefwxMTD4xSjKPDGZa7f\n5I4eGM9B1ICEobM9s5LG4lcYVIM8lu4klMvQJzrI5jWQkqJhsH9qJXtHVqFYZZSQQveD52LXaigr\nTWo2jaAaVXpQGMtFKRaCLG6GtyWh4xhprTYdtzzdS6PrCp/9uyC//PDcDhXXjSChgiBbgvgJ0mgt\nXaqzdOlCddJLPAf8/PDExMPjNLGpBdY1Qk8K7h+AvjQowmCo/SKiqd+zeGCAvkKIA2Y7uyOX4+/S\nEUULX8FCUyuEYgoTPU2o2DjTgsB4iRX5Q6xt2U13bhWZ0lY2aQlUAbdkoCvkTiKeTyrlEAqJ5zL7\nzofDvRpaxsRKCnekyAtQHWpW6PzqVzaVQTj3swqGcXLO9eFhi8OHq8TjCmvW+M4iZ73nM5kPnph4\neJxGNBVW1Llb0YSKDd+2/Oyb0Vm0JsIe9UqG0puxB2Okx1UC4QqFiIpQbYZLIQJGic7aXqpVHZ9W\nptwTxlkCmwIPoUiN4ep5LFcbSVkKGfuImDgODA7afO1rRRYvVvnQh+a/yPDeRyRWjw1OHvwCQiq+\n9hD9uwV2Dr7RDXt6JL/6tiD2CpL9lssODz5Y5JZbykSjKqYp2bnT5sorz4YFkV4013zxxMTD41Ui\n6IMg0Dnlo3sohXqZj/RIM0sa+lkUH+PJoQ1kCglMdHfhiJCUmwzIQ2i6wKLQEOlKnEw+TihUIigK\nDBVLTKTyGEoUo9G9zuFR+P49MDsNuaxghe/Fd/qm6TAyYtLU5MMwXphLy7Yhl4fde20ISwgJqFMh\nD6ZWhYoPLQHChO5+GJtm3mIyMWHyiU/0s2dPFV03uPTSGJ2dPh55pMIb3xg4C5I+emIyXzwx8fB4\nldlSu5afBB6kIHM4KPgck6wWJWnMUMhFEYZEqhJMsBWNvD9ExJ8hXp+inDWIihxBX5GRmXbSKT+B\nvMKySfjuBKxdAj+5D9oTkGhXGTZCvPUdL7bh1ltTPPJIjpUrA/zxHze+4LPf3gd3PQDRqICEApYK\nOcARbqXGEFgToBhw3mbJ4tb5/w2+//0penrKaJpCKlVmcDBALKYTCAhU9cTHn27caK7CmTZjQeGJ\niYfHq0ydaGbDRUmm7QoNvhGsbABL1/FhIoTjLqG3JfgFulom2jDL8roDjFabmNFrKUSSTNqrqFFs\nNmgVmq0AKQVufhr6C/DEJBQc2NgEqk9QKEgO7a9SW6vQ0eGO1JWKg5TuepCf/KSIzye44go3YWNj\nPbS3woXnq/ziPgk1ArICokBVgzwoDXDp+fCzf1ZRjp8k+JjouuvH0TQHIQTVqiCfd3jPe0IIcaZn\nJW6Rr66uE3fMi+Y6gicmHh6vMho6F2lb+e2+r7KqxaIvdw5q1iZbjmMEilgzEYRto2gWsY4ZYjM5\nJuN1TM/Uk9hSJOd/HcGSTnJvDUuFGw/8aMpN+tgUhYtWwt37oUaFN22CW39aobfXQdPgxhsNGhsV\n3va2GtauDdHW5ufb3y69wIl+7jnuNjENCUNhdkSAJtyIrqqEgiAcEtzwVvGKhATgne+sJZOx6e8v\n8+d/nmDHjgSRiPKKAgVOD95jrvniiYmHxxlgtW8XgfZxbvvRf3Jx2z46B9fxq8yV9Gqd6FaVqunD\nX1sitq9ISQlQVMLEV+Y5v0tDotOiB1GsGKN5SIYgW4KgAZGAu+1YCR+/COpj8PCvJPE4pNNQLEoA\ngkGV1avdnC8f/vCxVx421ML29ZLf3COoFkArSFAFtgPnLoY3nv/K+9/Q4OPTn170yk9w2vHEZL54\nYuLhcQZQEHTVvYu9jVfwP/5lmEohw8g5yygRoL59gtraEUJ6kYnReorLDbq2jrKpJc4OtZYxLFZr\nBlsvFPzySRiZgdcvh9EyjKTdJ2SvX+EKCcDVV/u4444qGzYodHS8eCqhqi89G/jCZxWuusaht1dQ\ntgTShvYO+Nf/LfAvxAwp88ITk/ngiYmHxxnkoouD+G5to28wQ2EsjOiQTIw2MnM4gRGrYPYaLFkz\nw6bYatq1AqNUqUXjPEJEgnDNziPnms7DWBYifuh4XqXhjg6V669/ZV7tFcsFv7hF4dv/Kdl3QLJ2\nrcJ7r4GWppPr99lOuWzT3T3PHDSvcTwx8fA4g8Rigv/1VwYf+Lsi+SGJ3K9C3MFW/eQGAxBx2LCy\nmQ+FFVYrAUo4hFBQePFsojbsbqeatkWC//6ps8WX8ergOuBPnAbGc8Af4RW6zzw8PE4VuzoF176n\nhvYOC802YQycQQ3NJ/if/03jq8s11vlUNAQR1GMKicfpwH4Zm8ezeDMTD48zjKHBZy6CXV0BbnkM\nxiZgTSvccAm0Rs+0da9VPAf8fPHExMPjLCCow2Wd7uZxNuCJyXzxxMTDw8PjmHhiMh88MfHw8PA4\ninLZort7+kybsaDwxMTDw8PjKAxDoasrdMJ2XjTXETwx8fDw8HgRns9kvnhi4uHh4XFMvEqL80FI\nKc+0DSdECDEFDJzi07YBg6f4nK8mC91+WPh9WOj2w8Lvw7Hsb5dS1p3MSYUQtwG1L6PptJTyspO5\n1h8KC0JMTgdCiKmT/Yc7kyx0+2Hh92Gh2w8Lvw8L3f4/JF7LK+DTZ9qAk2Sh2w8Lvw8L3X5Y+H1Y\n6Pb/wfBaFpPMmTbgJFno9sPC78NCtx8Wfh8Wuv1/MLyWxeRrZ9qAk2Sh2w8Lvw8L3X5Y+H1Y6Pb/\nwfCa9Zl4eHh4eJw6XsszEw8PDw+PU4QnJh4eHh4eJ40nJh4eHh4eJ40nJh4eHh4eJ40nJh4eHh4e\nJ83/ByxSI+kUx6CTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15f5f4c0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# next: housing prices.\n",
    "# color = price \n",
    "# radius = population\n",
    "# use predefined \"jet\" color map \n",
    "\n",
    "housing.plot(\n",
    "    kind=\"scatter\", \n",
    "    x=\"longitude\", \n",
    "    y=\"latitude\", \n",
    "    alpha=0.4,\n",
    "    #s=housing[\"population\"].apply(lambda n: n/100), \n",
    "    s=housing[\"population\"]/100,\n",
    "    label=\"population\",\n",
    "    c=\"median_house_value\", \n",
    "    cmap=plt.get_cmap(\"jet\"), \n",
    "    colorbar=True,\n",
    ")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.687160\n",
       "total_rooms           0.135097\n",
       "housing_median_age    0.114110\n",
       "households            0.064506\n",
       "total_bedrooms        0.047689\n",
       "population           -0.026920\n",
       "longitude            -0.047432\n",
       "latitude             -0.142724\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# next: look for correlatons to median house value.\n",
    "\n",
    "corr_matrix = housing.corr()\n",
    "\n",
    "corr_matrix['median_house_value'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5fc20f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5eda860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f602f898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5ff2860>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5f7dd68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f5e836d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f460e710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f45d50b8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15f45224e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f454ab38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f449ec88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f44e5898>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f15f44380b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f4450be0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f43c2da0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f15f43960f0>]], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XzxMpxV88dZq3bBuklDU5NN9k+1CewwtNam0PL1QcXmjy5ZcWuXVyACcIefZU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XvEqzCbp5LABL+oeenc5g6oKI3yRbsjg73d7k2Yp3HNjBH30rylSqwHA8yDsO7FjV9QpFJwmb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\noWQT1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655XQQQ0xQycY1MXCOmKiRjGo4fsFJ1ODdX4vMnZpgvmqQMnXsG0jx1YJBDozne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IlOsFkQCfe3aUkmMi5d//up3sGeokU6wynimzPZWoN1kZTiXqxU7L5dUuN1XbPN26UqpI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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15f6094978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# another way of looking for correlations: scatter_matrix\n",
    "# focus on top 3 factors from above\n",
    "\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "\n",
    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
    "\"housing_median_age\"]\n",
    "\n",
    "scatter_matrix(housing[attributes], figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f15f41b1a90>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bx2xVc2Mn4ytHA46H2eb+A/lqeLfP6uZ5FgGq2qp34QXXRzl1YxnkEXXjQrV9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\n1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bfvzvjmnQnWe6oq2DlHwJt5RU3YZD2huf6o7AbulbEW5p7A5YsICYcGbzWB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\nWCwWi2VXsIJjsVgsll3h/wNrxcnzKrmQVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15f43d72b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# median house value to median income seems to be the most promising.\n",
    "# let's zoom in.\n",
    "\n",
    "housing.plot(\n",
    "    kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
    "    alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.687160\n",
       "rooms_per_household         0.146285\n",
       "total_rooms                 0.135097\n",
       "housing_median_age          0.114110\n",
       "households                  0.064506\n",
       "total_bedrooms              0.047689\n",
       "population_per_household   -0.021985\n",
       "population                 -0.026920\n",
       "longitude                  -0.047432\n",
       "latitude                   -0.142724\n",
       "bedrooms_per_room          -0.259984\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine some attributes to create more useful ones\n",
    "# then rebuild the correlation matrix.\n",
    "\n",
    "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
    "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
    "housing[\"population_per_household\"]=housing[\"population\"]/housing[\"households\"]\n",
    "\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending=False)\n",
    "\n",
    "# *** NOTE: rooms_per_household corr (in book) show more improvement, ~0.199\n",
    "# compared to our 0.146. Not sure of root cause yet. ***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Data Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# revert to clean copy of stratified training dataset\n",
    "# separate predictors from labels\n",
    "\n",
    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
    "\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 'total bedrooms' has some missing values - fix\n",
    "# can use DataFrame dropna(), drop(), fillna()\n",
    "\n",
    "# use Scikit-Learn class to handle missing values\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Imputer(axis=0, copy=True, missing_values='NaN', strategy='median', verbose=0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop ocean_proximity attribute, since it's non-numeric.\n",
    "# then fit to training data.\n",
    "\n",
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n",
       "        1164.    ,   408.    ,     3.5409])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now what do we have?\n",
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -118.51  ,    34.26  ,    29.    ,  2119.5   ,   433.    ,\n",
       "        1164.    ,   408.    ,     3.5409])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# update training set by replacing missing values with learned medians\n",
    "X = imputer.transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16512 entries, 0 to 16511\n",
      "Data columns (total 8 columns):\n",
      "longitude             16512 non-null float64\n",
      "latitude              16512 non-null float64\n",
      "housing_median_age    16512 non-null float64\n",
      "total_rooms           16512 non-null float64\n",
      "total_bedrooms        16512 non-null float64\n",
      "population            16512 non-null float64\n",
      "households            16512 non-null float64\n",
      "median_income         16512 non-null float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "pd.DataFrame(X, columns=housing_num.columns).info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 4, ..., 1, 0, 3])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert ocean_proximity feature to numbers using LabelEncoder.\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoded = encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n"
     ]
    }
   ],
   "source": [
    "# how is 'ocean_proximity' mapped?\n",
    "print(encoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a better solution for categorical data: one-hot encoding\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()\n",
    "\n",
    "# output = SciPy sparse matrix, better for memory usage\n",
    "# if you need a dense NumPy array, call toarray()\n",
    "\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1,1))\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Label Binarization:](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html)\n",
    "- A shortcut (text categories => integer categories => one-hot vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ..., \n",
       "       [0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Transformers:\n",
    "\n",
    "* Create your own using SciKit-Learn classes\n",
    "* implement fit(), transform() and fit_transform() methods\n",
    "* (fit_transform comes for free by using TransformerMixin as a base class.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    \n",
    "    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
    "    \n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self # nothing else to do\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household      = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, \n",
    "                         rooms_per_household, \n",
    "                         population_per_household,\n",
    "                         bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, \n",
    "                         rooms_per_household, \n",
    "                         population_per_household]\n",
    "\n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling\n",
    "\n",
    "* Min-max scaling (normalization) = shift & rescale to [0,1]\n",
    "* SciKit MinMaxScaler will do this for you.\n",
    "* Standardization subtracts mean & divides by variance - result has unit variance\n",
    "* SciKit StandardScaler does this for you.\n",
    "\n",
    "### Pipelining\n",
    "\n",
    "* SciKit Pipeline class helps to standardize the sequence of transforms\n",
    "  you need for your project.\n",
    "* Pipelines = list of estimator steps. All but the last must be transformers\n",
    "  (they must have fit_transform() method.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# \"DataFrameSelector\" is a custom transformer class.\n",
    "# grabs the specified feature, drops the rest, converts the DF into a NumPy array.\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__ (self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    \n",
    "    def fit (self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform (self, X):\n",
    "        return X[self.attribute_names].values\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = ['ocean_proximity']\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('selector',      DataFrameSelector(num_attribs)),\n",
    "    ('imputer',       Imputer(strategy=\"median\")),\n",
    "    ('attribs_adder', CombinedAttributesAdder()),\n",
    "    ('std_scaler',    StandardScaler()),\n",
    "    ])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('selector',      DataFrameSelector(cat_attribs)),\n",
    "    ('label_binarizer', LabelBinarizer()),\n",
    "])\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list =[\n",
    "    ('num_pipeline', num_pipeline),\n",
    "    ('cat_pipeline', cat_pipeline)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15604281,  0.77194962,  0.74333089, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.17602483,  0.6596948 , -1.1653172 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 1.18684903, -1.34218285,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ..., \n",
       "       [ 1.58648943, -0.72478134, -1.56295222, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78221312, -0.85106801,  0.18664186, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-1.43579109,  0.99645926,  1.85670895, ...,  0.        ,\n",
       "         1.        ,  0.        ]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's try it out:\n",
    "\n",
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 16)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Note: \n",
    "    pip3 install sklearn-pandas => gets a DataFrameMapper class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Selection & Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's start with a linear regression\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions:\t [ 210644.60459286  317768.80697211  210956.43331178   59218.98886849\n",
      "  189747.55849879]\n",
      "labels:\t [286600.0, 340600.0, 196900.0, 46300.0, 254500.0]\n"
     ]
    }
   ],
   "source": [
    "# first try. NOT very accurate.\n",
    "\n",
    "some_data          = housing.iloc[:5]\n",
    "some_labels        = housing_labels.iloc[:5]\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print (\"predictions:\\t\", lin_reg.predict(some_data_prepared))\n",
    "print (\"labels:\\t\", list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "typical prediction error:\t 68628.1981985\n"
     ]
    }
   ],
   "source": [
    "# why? look at RMSE on whole training set.\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse             = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_rmse            = np.sqrt(lin_mse)\n",
    "\n",
    "print (\"typical prediction error:\\t\", lin_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "           splitter='best')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hmmm. Not good. Underfit situation. \n",
    "# Let's try a more powerful model, like a Decision Tree.\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "typical prediction error:\t 0.0\n"
     ]
    }
   ],
   "source": [
    "# Zero error? No way...\n",
    "\n",
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "\n",
    "print (\"typical prediction error:\\t\", tree_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 69368.62190153  66248.56520386  72284.6557095   68417.57732406\n",
      "  70049.44916939  74941.75765797  70236.59348749  69466.63688954\n",
      "  76140.22952307  70217.59755116]\n",
      "Mean: 70737.1684418\n",
      "Standard deviation: 2815.58298405\n"
     ]
    }
   ],
   "source": [
    "# Use K-fold cross-validation\n",
    "# Train & eval Decision Tree model against 10 splits of training dataset\n",
    "# Returns 10 evaluation scores.\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(\n",
    "    tree_reg, \n",
    "    housing_prepared, \n",
    "    housing_labels,\n",
    "    scoring=\"neg_mean_squared_error\", \n",
    "    cv=10)\n",
    "\n",
    "rmse_scores = np.sqrt(-scores)\n",
    "\n",
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "    \n",
    "display_scores(rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 66782.73843989  66960.118071    70347.95244419  74739.57052552\n",
      "  68031.13388938  71193.84183426  64969.63056405  68281.61137997\n",
      "  71552.91566558  67665.10082067]\n",
      "Mean: 69052.4613635\n",
      "Standard deviation: 2731.6740018\n"
     ]
    }
   ],
   "source": [
    "# So, Decision Tree RMSE: mean ~71097, stdev 2165 (still sucks.)\n",
    "# compare to earlier Linear Regression:\n",
    "\n",
    "lin_scores = cross_val_score(\n",
    "    lin_reg,\n",
    "    housing_prepared,\n",
    "    housing_labels,\n",
    "    scoring=\"neg_mean_squared_error\",\n",
    "    cv=10)\n",
    "\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 52480.82629458  50035.41358467  53747.69332484  55053.95194112\n",
      "  51800.65152945  55919.01705209  52226.75176017  50912.82366116\n",
      "  55708.47271341  51931.81080304]\n",
      "Mean: 52981.7412665\n",
      "Standard deviation: 1929.32402243\n"
     ]
    }
   ],
   "source": [
    "# Yep, DT overfit is just about as bad. (RMSE mean 69052, stdev 2731)\n",
    "# Let's try a RandomForest.\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(housing_prepared, housing_labels)\n",
    "\n",
    "forest_scores = cross_val_score(\n",
    "    forest_reg,\n",
    "    housing_prepared,\n",
    "    housing_labels,\n",
    "    scoring=\"neg_mean_squared_error\",\n",
    "    cv=10)\n",
    "\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# OK, RandomForest is a little better.\n",
    "# RMSE mean ~52495, stdev ~1569"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fine-Tuning Model with Grid Search of Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid=[{'max_features': [2, 4, 6, 8], 'n_estimators': [3, 10, 30]}, {'bootstrap': [False], 'max_features': [2, 3, 4], 'n_estimators': [3, 10]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], \n",
    "     'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], # bootstrap = True = default setting\n",
    "     'n_estimators': [3, 10], \n",
    "     'max_features': [2, 3, 4]},\n",
    "]\n",
    "\n",
    "forest_reg  = RandomForestRegressor()\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    forest_reg, \n",
    "    param_grid, \n",
    "    cv=5,\n",
    "    scoring = 'neg_mean_squared_error')\n",
    "\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best combination of parameters?\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=6, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best estimator?\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63492.9975584 {'max_features': 2, 'n_estimators': 3}\n",
      "55677.1037862 {'max_features': 2, 'n_estimators': 10}\n",
      "52917.801725 {'max_features': 2, 'n_estimators': 30}\n",
      "60442.2787178 {'max_features': 4, 'n_estimators': 3}\n",
      "53209.7111283 {'max_features': 4, 'n_estimators': 10}\n",
      "50621.1191846 {'max_features': 4, 'n_estimators': 30}\n",
      "58591.8196313 {'max_features': 6, 'n_estimators': 3}\n",
      "52353.3606044 {'max_features': 6, 'n_estimators': 10}\n",
      "49838.3807 {'max_features': 6, 'n_estimators': 30}\n",
      "58615.6100561 {'max_features': 8, 'n_estimators': 3}\n",
      "51726.2593734 {'max_features': 8, 'n_estimators': 10}\n",
      "50074.3050139 {'max_features': 8, 'n_estimators': 30}\n",
      "62010.5215854 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54852.7770725 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "60246.2164711 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52752.4109521 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "58355.1846204 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "51724.6800894 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "# Evaluation scores:\n",
    "\n",
    "cvres = grid_search.cv_results_\n",
    "\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"],\n",
    "                              cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# best solution:\n",
    "# max_features = 6, n_estimators = 30 (RMSE ~49,960)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  8.00229340e-02,   7.13499357e-02,   4.21346911e-02,\n",
       "         1.73340009e-02,   1.55694906e-02,   1.76527489e-02,\n",
       "         1.56813711e-02,   3.21068169e-01,   7.54675530e-02,\n",
       "         1.07645094e-01,   5.74608930e-02,   1.47327045e-02,\n",
       "         1.57310792e-01,   9.20951468e-05,   2.63317542e-03,\n",
       "         3.84435167e-03])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.32106816893273865, 'median_income'),\n",
       " (0.15731079177984286, 'INLAND'),\n",
       " (0.10764509417315272, 'pop_per_hhold'),\n",
       " (0.080022934000105003, 'longitude'),\n",
       " (0.075467553036607335, 'rooms_per_hhold'),\n",
       " (0.071349935674308126, 'latitude'),\n",
       " (0.057460893036370447, 'bedrooms_per_room'),\n",
       " (0.04213469106714228, 'housing_median_age'),\n",
       " (0.017652748894983483, 'population'),\n",
       " (0.017334000890698829, 'total_rooms'),\n",
       " (0.015681371107232313, 'households'),\n",
       " (0.015569490624941605, 'total_bedrooms'),\n",
       " (0.014732704544371122, '<1H OCEAN'),\n",
       " (0.0038443516681782959, 'NEAR OCEAN'),\n",
       " (0.00263317542255579, 'NEAR BAY'),\n",
       " (9.2095146771177451e-05, 'ISLAND')]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display feature \"importance\" scores next to their names:\n",
    "\n",
    "extra_attribs       = [\"rooms_per_hhold\", \"pop_per_hhold\", \"bedrooms_per_room\"]\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes          = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "\n",
    "sorted(zip(feature_importances, attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time to Eval System on Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47574.62166586089"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test          = strat_test_set.drop(\"median_house_value\", axis=1)\n",
    "y_test          = strat_test_set[\"median_house_value\"].copy()\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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